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Second-order contexts from lexical substitutes for few-shot learning of word representations
2019
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*
There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word firstorder contexts, whereas our method aggregates contexts as
doi:10.18653/v1/s19-1007
dblp:conf/starsem/LiuMK19
fatcat:3sdyzbzzgff3fgujoh7la2tg44